CRII: RI: Efficient Structure Learning and Approximation of Networks of Causally Interacting Processes

CRII：RI：因果交互过程网络的有效结构学习和逼近

• 批准号: 1566513
• 负责人: Christopher Quinn
• 金额: $ 17.5万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1566513&HistoricalAwards=false
• 关键词: CRII; RI; Efficient; Structure; Learning

The study of networks is important in numerous scientific domains: neuroscience, microbiology, social science, and economics, to name a few. A major challenge in these fields is to identify causal influences in the networks. Experimentation can directly determine causal influences. However, it can be more costly and less practical than passively recording activity in the network and inferring influences from those observations. There are numerous methods that can identify correlations from observational data, though identifying causal relationships often requires expert knowledge or strong modeling assumptions. There is a need for computationally efficient and statistically robust causal inference methods to extract relevant information from network time-series data to facilitate human analysis.This research aims to significantly advance the state of the art in inferring causal influences between time-series. The research develops new and efficient algorithms to learn and approximate the structure of a recently proposed probabilistic graphical model: the directed information graph. The algorithms find optimal or near-optimal approximations of the network topology that have user-controlled sparsity levels, such as the number of edges in the graph or the amount of computation performed. The quality of approximation is measured using Kullback-Leibler divergence. The work also involves proving correctness of the algorithms and developing variations that find provably-good approximations which are robust to noisy or limited data. To achieve these goals, the project develops new bounds for directed information.

对网络的研究在许多科学领域都很重要：神经科学、微生物学、社会科学和经济学，仅举几例。这些领域的一个主要挑战是确定网络中的因果影响。实验可以直接确定因果影响。然而，与被动地记录网络中的活动并从这些观测中推断影响相比，它可能成本更高，实用性更差。有许多方法可以从观测数据中识别相关性，尽管识别因果关系通常需要专业知识或强大的建模假设。需要一种计算高效、统计稳健的因果推理方法从网络时间序列数据中提取相关信息以便于人类分析，本研究旨在显著提高推断时间序列之间因果影响的技术水平。这项研究开发了新的高效算法来学习和近似最近提出的一种概率图形模型的结构：有向信息图。该算法找到具有用户控制的稀疏级别的网络拓扑的最佳或接近最佳的近似，例如图中的边数或执行的计算量。使用Kullback-Leibler散度来衡量近似的质量。这项工作还包括证明算法的正确性，并开发找到可证明良好的近似的变体，这些近似对噪声或有限的数据是稳健的。为了实现这些目标，该项目为定向信息开发了新的界限。

项目成果

We investigate a novel duality for scalar Gaussian multiple access channels and broadcast channels. The duality we explore is based on shared partial information quantities (e.g. synergy and redundancy). Using lattice theory, we establish a crossover co

我们研究了标量高斯多址信道和广播信道的新颖对偶性。

• 发表时间: 2020
• 期刊: International Symposium on Information Theory and Its Applications
• 作者: Niu, Xueyan;Quinn, Christopher J.
• 通讯作者: Quinn, Christopher J.